We specifically looked at participants that did not undego any social modelling; 69 participants, each with 51 variables recorded. Our project focused the following variables:
Baseline SSQ (BSSQ), Active SSQ (ASSQ), and SSQ_Fullof 16 symptoms (quantitative, discrete): self-reported symptom severity before and after undergoing VR respectively, on a scale of 1 to 10.
Participants’ VRexperience (qualitative, nominal); sorted by experience to see how symptoms differed.
age of participants (quantitative, discrete); they were then sorted into age groups.
The change (\(\Delta\)) between BSSQ and ASSQ was calculated for each participant for each symptom, and we took the average \(\Delta\) for each symptom for each group (VRexperience and age_group) (quantitative, discrete).
data_experience =select(withVRexperience, age_group)exp_counted = data_experience %>%count(age_group)vr_pie =plot_ly(exp_counted, labels =~age_group, values =~n,type ='pie')vr_pie <- vr_pie %>%layout(title ='Distribution of age groups for those with VR experience',showlegend =TRUE)vr_pie
Code
data_experience =select(noVRexperience, age_group)exp_counted = data_experience %>%count(age_group)vr_pie =plot_ly(exp_counted, labels =~age_group, values =~n,type ='pie')vr_pie <- vr_pie %>%layout(title ='Distribution of age groups for those without VR experience',showlegend =TRUE)vr_pie
Limitations
Self-reporting bias for both BSSQ and ASSQ which makes the values prone to being over or underestimates by each participant.
VRexperience is a binary qualitative classification, it is not descriptive of the nature, amount or frequency, and that those with more than 10 VR experiences were excluded.
Research Question
What is the effect of past VR experience on the symptoms experienced by people?
Code
plt =ggplot(filteredData, aes(x = VRexperience, y = ssq_full, fill = VRexperience)) +geom_boxplot() +theme_minimal() +labs(x ="VR Experience", y ="Full SSQ")ggplotly(plt)
Those with and without VR experience reported varied severity of symptoms. Interestingly, the VR experienced group saw a greater median ssq_full, at 7.0 compared to -0.5 for those without experience, indicating those with experience reported more severe symptoms afterwards. VR experienced participants also showed a greater IQR than those without, at 16.25 and 8.5 respectively, pointing to experienced users reporting less consistent symptom severity. That is, the difference in average rating from the Simulator Sickness Questionnaire (SSQ) is consistently lower for less experienced users. With respect to age, the scatter-plot we have has a very low correlation of [INSERT CORRELATION FOR YES AND NO EXPERIECNE CALCULATION HERE], implying that the data will not fit to a linear model, and there conclusively exists no correlation between age and severity of symptoms, across both having some and zero past experience with VR.
With regards to gender, source [1] (apa this), their results “indicate excruciating symptoms of motion sickness in females”, in contrast to our results which depict no difference in reported sickness with respect to gender. This may be a case of difference in findings, as both Saunders’s research paper and [1]’s had a considerably small group of test subjects, implying that both suggestions may be equally true, and would require a larger group in more trials to verify.
The spider-chart above reinforces what we see in the box-plot, with VR experienced users all indicating a higher average \(\Delta\) for almost all of the 16 symptoms.
To further investigate why our conclusions contradicts research, we investigate a potential confounding variable: age.
plt2 =ggplot(withVRexperience, aes(x = age_group, y = ssq_full, fill = age_group)) +geom_boxplot() +labs(title ="Full SSQ for each age group (With experience)", y ="Full SSQ", x ="Age Group") +ylim(-10, 60)ggplotly(plt2)
Code
plt2 =ggplot(noVRexperience, aes(x = age_group, y = ssq_full, fill = age_group)) +geom_boxplot() +labs(title ="Full SSQ Scores for each age group (No experience)", y ="Full SSQ", x ="Age Group") +ylim(-10, 60)ggplotly(plt2)
Code
library(tidyverse)library(plotly)plt =ggplot(filteredData, aes(x =as.factor(age_group) , y = ssq_full, group = VRexperience, fill = VRexperience)) +geom_boxplot(position ="dodge") +labs(x ="Age Group", y ="Full SSQ", title ="Full SSQ Scores by Age Group (both groups)") +ylim(-10, 60)ggplotly(plt) %>%layout(boxmode ="group")
Code
plt2 =ggplot(filteredData, aes(x = age_group, y = ssq_full, fill = age_group)) +geom_boxplot() +labs(title ="Full SSQ Scores for each age group", y ="Full SSQ", x ="Age Group") +ylim(-10, 60)ggplotly(plt2)
As the boxplots show, age does not seem to be a confounding variable explaining the increase in symptoms that arise with previous VR experience. There appears to be substantial overlap between the SSQ scores of different age groups across both the “With Experience” and “No Experience” conditions. In both sets of boxplots, the medians remain relatively close together, with no consistent trend among increasing age groups.
Notably, while the spread of the SSQ scores (as shown by the IQR and outliers) varies, there seems to be no consistent pattern across age groups. For example, in the “With Experience” condition, the 16-21 age group has a greater IQR than the 22-29 age group, but then the 22-29 group has a lower IQR than the 30-37 group, showing no consistent trend.
The comparison plot emphasises that across all age groups, participants with previous VR experience reported more severe symptoms. Despite some variation in spread within each group, the general shift upward in medians for experienced participants suggests a relationship between experience and symptom severity. However, given the overlap in distributions and lack of consistent change in medians across age groups, age does not appear to be a decisive confounding factor. The observations found here conflict with previous research that suggests older age groups experience more severe cybersickness (Oh & Son, 2022). A Possible justification for this is that the age sample sizes are not equal. For example, there are 29 participants in the 30 to 37 age group but only 7 in the 16 to 21 age group. This limits the reliability of comparisons made across age groups, as smaller sample sizes can lead to greater variability, making it harder to detect trends.
Finally, the combined graph reinforces the idea that age does not seem to be a confounding factor as there is significant overlap in Full SSQ between all age groups. Holistically, while we do find that past VR experience correlates with higher reported symptoms, age does not provide a consistent explanatory variable for this increase. Like other complex human responses, factors beyond age may be more influential in determining symptom severity.
Code
ggplot(filteredData) +geom_boxplot(aes(x ="Discomfort", y = d_discomfort, fill = VRexperience)) +geom_boxplot(aes(x ="Fatigue", y = d_fatigue, fill = VRexperience)) +geom_boxplot(aes(x ="Headache", y = d_headache, fill = VRexperience)) +geom_boxplot(aes(x ="Eyestrain", y = d_eyestrain, fill = VRexperience)) +geom_boxplot(aes(x ="Difficulty Focusing", y = d_difficulty_focusing, fill = VRexperience)) +geom_boxplot(aes(x ="Salivation", y = d_salivation, fill = VRexperience)) +geom_boxplot(aes(x ="Sweating", y = d_sweating, fill = VRexperience)) +geom_boxplot(aes(x ="Nausea", y = d_nausea, fill = VRexperience)) +geom_boxplot(aes(x ="Difficulty Concentrating", y = d_difficulty_concentrating, fill = VRexperience)) +geom_boxplot(aes(x ="Fullness of Head", y = d_fullness_of_head, fill = VRexperience)) +geom_boxplot(aes(x ="Blurred Vision", y = d_blurred_vision, fill = VRexperience)) +geom_boxplot(aes(x ="Dizziness (o)", y = d_dizziness_o, fill = VRexperience)) +geom_boxplot(aes(x ="Dizziness (c)", y = d_dizziness_c, fill = VRexperience)) +geom_boxplot(aes(x ="Vertigo", y = d_vertigo, fill = VRexperience)) +geom_boxplot(aes(x ="Stomach Awareness", y = d_stomach_awareness, fill = VRexperience)) +theme_minimal() +labs(title ="VR Experience and Symptom Change",x ="Symptom",y ="Change in Severity") +theme(axis.text.x =element_text(angle =45, hjust =1))
Code
library(tidyverse)library(plotly)plt =ggplot(filteredData, aes(x =as.factor(gender) , y = ssq_full, group = VRexperience, fill = VRexperience)) +geom_boxplot(position ="dodge") +labs(x ="Gender", y ="Full SSQ")ggplotly(plt) %>%layout(boxmode ="group")
-reason out in short sentences possible causes for why age doesnt correlate (ie smaller sample space)
- other issues might also lie in how reporting sickness is inherently clunky (like wtf is the burping questionnare bro come on)
-might try and explain comical amounts of box plots that are just a single line (challenge problem for me to answer if i dare)
